CANDOR-Bench: Benchmarking In-Memory Continuous ANNS under Dynamic Open-World Streams [Experiments & Analysis]
Summary: CANDOR-Bench benchmarks in-memory continuous ANNS under dynamic open-world streams, extending Big-ANN with high-rate ingestion, drift/modality shift, noise, and concurrent query-update execution. Targets the missing regime where static ANN benchmarks miss churn, distribution shift, and update/retrieval tradeoffs. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Mingqi Wang (Huazhong University of Science and Technology)
- 2. Junyao Dong (Nanyang Technological University)
- 3. Zhuoyan Wu (National University of Singapore)
- 4. Jun Liu (Huazhong University of Science and Technology)
- 5. Ruicheng Zhang (Huazhong University of Science and Technology)
- 6. Jianjun Zhao (Huazhong University of Science and Technology)
- 7. Ruipeng Wan (Huazhong University of Science and Technology)
- 8. Xinyan Lei (Huazhong University of Science and Technology)
- 9. Shuhao Zhang (Huazhong University of Science and Technology)
- 10. Bolong Zheng (Huazhong University of Science and Technology)
- 11. Haikun Liu (Huazhong University of Science and Technology)
- 12. Xiaofei Liao (Huazhong University of Science and Technology)
- 13. Hai Jin (Huazhong University of Science and Technology)
BibTeX Citation
@inproceedings{wang_sigmod26,
title = {{CANDOR-Bench: Benchmarking In-Memory Continuous ANNS under Dynamic Open-World Streams [Experiments \& Analysis]}},
author = {Wang, Mingqi and Dong, Junyao and Wu, Zhuoyan and Liu, Jun and Zhang, Ruicheng and Zhao, Jianjun and Wan, Ruipeng and Lei, Xinyan and Zhang, Shuhao and Zheng, Bolong and Liu, Haikun and Liao, Xiaofei and Jin, Hai},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786630},
url = {https://dl.acm.org/doi/10.1145/3786630},
year = {2026}
}
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